CN112995510A - Method and system for detecting environment light of security monitoring camera - Google Patents
Method and system for detecting environment light of security monitoring camera Download PDFInfo
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- CN112995510A CN112995510A CN202110212858.4A CN202110212858A CN112995510A CN 112995510 A CN112995510 A CN 112995510A CN 202110212858 A CN202110212858 A CN 202110212858A CN 112995510 A CN112995510 A CN 112995510A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
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- G03B15/00—Special procedures for taking photographs; Apparatus therefor
- G03B15/02—Illuminating scene
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract
The application relates to a security monitoring camera environment light detection method and system, wherein the method comprises the steps of obtaining the theoretical illumination intensity of the current monitoring scene detected by a light sensor in real time; judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene; if so, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene; judging whether the current monitoring scene meets a second condition for starting the infrared supplementary lighting of the monitoring camera or not based on the actual illumination intensity; and if so, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera to start the infrared light supplement. The method and the device can improve the accuracy of the detection of the ambient light.
Description
Technical Field
The application relates to the technical field of ambient light detection, in particular to a method and a system for detecting ambient light of a security monitoring camera.
Background
In the security protection control field, surveillance camera machine wide application is in schools, the district, the trade circle, the mill, and places such as street, at present, infrared camera machine utilizes night vision function representation more outstanding, daytime, surveillance camera machine monitors the control scene under the effect of sun natural light and shoots, night has arrived, under no visible light or shimmer dark environment, surveillance camera machine adopts infrared emission module initiative to project the infrared light on the object, infrared light gets into the camera lens after the object reflection and images, make monitored control system can shoot the control to the surrounding environment 24 hours.
Because the infrared light filling lamp of surveillance camera machine is not always in the on state, when ambient light weakens, the surveillance camera machine generally adopts the light sensor who carries on to carry out illumination intensity and detects, whether confirm to open infrared light filling lamp, but when meetting the changeable bad condition of surveillance environment weather or light sensor live time overlength, the inaccurate condition of illumination intensity that light sensor detected can appear, consequently, the inventor thinks that there is further improvement space in surveillance environment illumination detection mode still.
Disclosure of Invention
In order to solve the problem of inaccurate illumination detection of a monitoring environment in the prior art, the application provides an environmental light detection method and system for a security monitoring camera.
In a first aspect, the application provides a method for detecting ambient light of a security monitoring camera, which adopts the following technical scheme:
a security monitoring camera environment light detection method comprises the following steps:
acquiring the theoretical illumination intensity of the current monitoring scene detected by a light sensor in real time;
judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared supplementary lighting or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene;
if the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
judging whether the current monitoring scene meets a second condition that the monitoring camera starts infrared supplementary lighting or not based on the actual illumination intensity;
and if the current monitoring scene meets a second condition that the monitoring camera starts infrared light supplement, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement.
By adopting the technical scheme, in addition to the theoretical illumination intensity of the current monitoring scene detected by the light sensor, after the theoretical illumination intensity is judged to reach the first condition of starting the infrared supplementary lighting, the actual illumination intensity of the current monitoring scene is obtained by obtaining the current monitoring image set for analysis, and the accuracy of detecting the ambient light can be improved by combining the judgment of the actual illumination intensity, so that whether the monitoring camera starts the second condition of the infrared supplementary lighting or not is determined, and when the second condition is also met, the infrared supplementary lighting is started; in addition, the current monitoring image set is subjected to learning analysis by utilizing the deep learning model, so that the actual illumination intensity of the current monitoring scene can be acquired more accurately and rapidly.
Optionally, based on the theoretical illumination intensity, determining whether the current monitored scene meets a first condition for starting the infrared light supplement of the monitoring camera includes:
judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not;
and if the theoretical illumination intensity is lower than a preset first intensity threshold value, determining that the current monitoring scene meets a first condition that the monitoring camera starts infrared supplementary lighting.
By adopting the technical scheme, when the weather light becomes weak or gradually becomes black, whether the theoretical illumination intensity obtained by judging the detection of the light sensor is lower than the first intensity threshold value or not can be preliminarily determined that the current monitoring scene meets the first condition that the monitoring camera starts the infrared light supplement, so that a judgment mechanism is preliminarily started.
Optionally, analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model, and acquiring the actual illumination intensity of the current monitoring scene, including:
training and learning the current monitoring image set by using a preset deep learning model, and outputting the original illumination intensity of each current monitoring image;
and counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the counting result, and taking the average illumination intensity as the actual illumination intensity.
By adopting the technical scheme, the illumination condition of the current monitoring scene is analyzed from the monitoring visual angle by performing deep learning analysis on the current monitoring image set acquired by shooting of the monitoring camera, and the actual illumination model of the current monitoring scene can be acquired more quickly and accurately through the deep learning model; in addition, by counting the average value of the original illumination intensities of the current monitored image, the average illumination intensity can be taken as the actual illumination intensity.
Optionally, based on the actual illumination intensity, determining whether the current monitored scene meets a second condition that the monitoring camera starts the infrared supplementary lighting, including:
judging whether the actual illumination intensity is lower than a preset second intensity threshold value, wherein the second intensity threshold value is smaller than or equal to the first intensity threshold value;
and if the actual illumination intensity is lower than a preset second intensity threshold value, determining that the current monitoring image meets a second condition that the monitoring camera starts infrared supplementary lighting.
By adopting the technical scheme, whether the actual illumination intensity is lower than the second intensity threshold value or not can be judged, whether the current monitoring scene meets the second condition that the monitoring camera starts the infrared light supplement or not can be judged, and therefore the environment light detection of the current monitoring scene is facilitated accurately, and whether the infrared light supplement is started or not is further facilitated to be determined.
Optionally, the infrared light supplement instruction is sent to a controller of the monitoring camera to start the infrared light supplement, and the method further includes:
calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity;
judging that the light intensity error value exceeds a preset error threshold value;
and if the light intensity error value exceeds a preset error threshold value, generating a light intensity correction instruction, and sending the light intensity correction instruction to the light ray sensor.
By adopting the technical scheme, the light sensor is corrected according to the light intensity error condition between the theoretical illumination intensity and the actual illumination intensity of the current monitoring scene, so that the subsequent detection result of the light sensor is more accurate.
In a second aspect, the present application provides a security surveillance camera environment light detection system, which adopts the following technical scheme:
a security surveillance camera ambient light detection system, the system comprising:
the detection acquisition module is used for acquiring the theoretical illumination intensity of the current monitoring scene detected by the light sensor in real time;
the first judging module is used for judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared supplementary lighting or not based on the theoretical illumination intensity, and the monitoring camera is used for monitoring the current monitoring scene;
the image analysis module is used for acquiring a current monitoring image set of the current monitoring scene if the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement, and performing illumination intensity analysis on the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
the second judging module is used for judging whether the current monitoring scene meets a second condition that the monitoring camera starts infrared supplementary lighting or not based on the actual illumination intensity;
and the light supplement module is used for generating an infrared light supplement instruction if the current monitoring scene meets a second condition that the monitoring camera starts infrared light supplement, and sending the infrared light supplement instruction to the controller of the monitoring camera so as to start the infrared light supplement.
By adopting the technical scheme, in addition to the theoretical illumination intensity of the current monitoring scene detected by the light sensor, after the theoretical illumination intensity is judged to reach the first condition of starting the infrared supplementary lighting, the actual illumination intensity of the current monitoring scene is obtained by obtaining the current monitoring image set for analysis, and the accuracy of detecting the ambient light can be improved by combining the judgment of the actual illumination intensity, so that whether the monitoring camera starts the second condition of the infrared supplementary lighting or not is determined, and when the second condition is also met, the infrared supplementary lighting is started; in addition, the current monitoring image set is subjected to learning analysis by utilizing the deep learning model, so that the actual illumination intensity of the current monitoring scene can be acquired more accurately and rapidly.
Optionally, the first determining module includes:
the first judging unit is used for judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not;
and the first determining unit is used for determining that the current monitoring scene meets a first condition that the monitoring camera starts the infrared supplementary lighting if the theoretical illumination intensity is lower than a preset first intensity threshold.
By adopting the technical scheme, when the weather light is weakened or gradually becomes black, whether the theoretical illumination intensity obtained by detecting the light sensor is lower than the first intensity threshold value or not can be preliminarily determined, and the current monitoring scene meets the first condition that the monitoring camera starts the infrared light supplement, so that a judgment mechanism is preliminarily started.
Optionally, the image analysis module includes:
the learning analysis unit is used for training and learning the current monitoring image set by using a preset deep learning model and outputting the original illumination intensity of each current monitoring image;
and the statistical unit is used for counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the statistical result, and taking the average illumination intensity as the actual illumination intensity.
By adopting the technical scheme, the illumination condition of the current monitoring scene is analyzed from the monitoring visual angle by performing deep learning analysis on the current monitoring image set acquired by shooting of the monitoring camera, and the actual illumination model of the current monitoring scene can be acquired more quickly and accurately through the deep learning model; in addition, by counting the average value of the original illumination intensity of each of the currently monitored images, the average illumination intensity can be taken as the actual illumination intensity.
Optionally, the second determining module includes:
a second judging unit, configured to judge whether the actual illumination intensity is lower than a preset second intensity threshold, where the second intensity threshold is less than or equal to the first intensity threshold;
and the second determining unit is used for determining that the current monitoring image meets a second condition that the monitoring camera starts the infrared supplementary lighting if the actual illumination intensity is lower than a preset second intensity threshold.
By adopting the technical scheme, whether the actual illumination intensity is lower than the second intensity threshold value or not can be judged, whether the current monitoring scene meets the second condition that the monitoring camera starts the infrared light supplement or not can be judged, and therefore the environment light detection of the current monitoring scene is facilitated accurately, and whether the infrared light supplement is started or not is further facilitated to be determined.
Optionally, the system further includes:
the error calculation module is used for calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity;
the error judgment module is used for judging whether the light intensity error value exceeds a preset error threshold value or not;
and the correction module is used for generating a light intensity correction instruction if the strong error value exceeds a preset error threshold value, and sending the light intensity correction instruction to the light ray sensor.
By adopting the technical scheme, the light sensor is corrected according to the light intensity error condition between the theoretical illumination intensity and the actual illumination intensity of the current monitoring scene, so that the subsequent detection result of the light sensor is more accurate.
In a third aspect, the present application provides a computer device, which adopts the following technical solution:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the security surveillance camera ambient light detection method when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the security surveillance camera ambient light detection method.
Drawings
FIG. 1 is a flowchart of an implementation of a method for detecting ambient light of a security monitoring camera according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of an ambient light detection system of a security monitoring camera according to an embodiment of the present application;
FIG. 3 is a functional block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-3.
As shown in fig. 1, an embodiment of the present application discloses a security monitoring camera environment light detection method, including:
s1: and acquiring the theoretical illumination intensity of the current monitoring scene detected by the light sensor in real time.
In this embodiment, the current monitoring scene refers to a field monitored by the monitoring camera, such as a school, a community, a factory, or a hospital; the theoretical illumination intensity refers to the ambient illumination intensity detected by the light sensor.
Specifically, a monitoring camera is arranged in the current monitoring scene, a light sensor is installed in the monitoring camera, the light sensor detects the current monitoring scene in real time and uploads a detection result to a cloud server through wireless communication, and the cloud server receives the detection result and takes the detection result as the theoretical illumination intensity of the current monitoring scene.
S2: and judging whether the current monitoring scene meets a first condition that the monitoring camera starts infrared supplementary lighting or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene.
In the present embodiment, the first condition refers to a determination condition set for the theoretical illumination intensity.
Specifically, whether the current monitoring scene meets the first condition that the monitoring camera starts the infrared light supplement is judged, and the method comprises the following steps:
judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not; if the theoretical illumination intensity is lower than a preset first intensity threshold value, determining that the current monitoring scene meets a first condition that the monitoring camera starts infrared supplementary lighting; and if the theoretical illumination intensity is higher than or equal to a preset first intensity threshold value, determining that the current monitored scene does not meet a first condition for starting the infrared supplementary lighting of the monitoring camera.
It should be noted that the first intensity threshold refers to an illumination intensity threshold set for a theoretical illumination intensity, and the unit is lux; the method includes the steps that a prestored first intensity threshold value is obtained from a database, theoretical illumination intensity is compared with the first intensity threshold value, the possibility that light of a current monitoring scene is weakened is shown aiming at the condition that the theoretical illumination intensity is smaller than the first intensity threshold value, it can be determined that the current monitoring scene meets a first condition that a monitoring camera starts an infrared light supplement lamp to supplement light, and it needs to be shown that besides the first condition, follow-up further judgment is needed, and then the ambient illumination of the current monitoring scene can be accurately judged.
S3: if the current monitoring scene meets the first condition that the monitoring camera starts the infrared light supplement, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene.
In this embodiment, the current monitoring image set refers to an image set of a video shot for a current monitoring scene.
Specifically, according to the determination result in step S2, if the current monitoring scene meets the first condition that the monitoring camera starts the infrared light supplement, continuing further analysis, specifically, obtaining a monitoring video of the current monitoring scene, performing framing processing on the monitoring video to obtain a current monitoring image set, then obtaining a pre-stored deep learning model from the database, and performing learning analysis on the current monitoring image set by using the deep learning model.
In this embodiment, a preset deep learning model is used to perform illumination intensity analysis on a current monitoring image set, so as to obtain an actual illumination intensity of a current monitoring scene, which specifically includes:
and training and learning the current monitoring image set by using a preset deep learning model, and outputting the original illumination intensity of each current monitoring image.
And counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the counting result, and taking the average illumination intensity as the actual illumination intensity.
In this embodiment, the deep learning model may adopt a cnn (volumetric Neural networks) convolutional Neural network, the deep learning model obtains a corresponding relationship between information of a captured image and illumination intensity by training a large number of captured images, which may be multiple images captured under a certain lighting condition, and the deep learning model based on the corresponding relationship may automatically identify the original illumination intensity of each current monitored image, thereby counting the average illumination intensity of the current monitored image set, and taking the average illumination intensity as the actual illumination intensity.
S4: and judging whether the current monitoring scene meets a second condition that the monitoring camera starts the infrared supplementary lighting or not based on the actual illumination intensity.
In the present embodiment, the second condition refers to a determination condition set for the actual illumination intensity.
Specifically, whether the current monitoring scene meets the second condition that the monitoring camera starts the infrared light supplement is judged, and the method comprises the following steps:
and judging whether the actual illumination intensity is lower than a preset second intensity threshold value, wherein the second intensity threshold value is smaller than or equal to the first intensity threshold value.
And if the actual illumination intensity is lower than a preset second intensity threshold value, determining that the current monitoring image meets a second condition that the monitoring camera starts the infrared supplementary lighting.
It should be noted that the second intensity threshold refers to an illumination intensity threshold set for the actual illumination intensity, and in this embodiment, the second intensity threshold is set to be less than or equal to the first intensity threshold,
the accuracy of detecting the ambient light intensity is improved; if the actual illumination intensity is lower than the preset second intensity threshold, it is indicated that when the ambient illumination intensity obtained according to the analysis of the current monitoring image is dark, the current monitoring image is likely to have the possibility of being unclear, so that the ambient illumination intensity obtained by analyzing the current monitoring image has more practical significance, and the monitoring of whether the shot picture is clear or ideal is facilitated. Therefore, when the actual illumination intensity is lower than the second intensity threshold, it can be determined that the current monitoring image meets the second condition that the monitoring camera starts the infrared supplementary lighting.
S5: and if the current monitoring scene meets a second condition that the monitoring camera starts the infrared light supplement, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement.
In this embodiment, the infrared light supplement instruction refers to a control instruction for turning on an infrared light supplement lamp of the monitoring camera.
Specifically, according to the determination result in step S4, if the current monitored scene meets the second condition that the monitoring camera starts the infrared light supplement, which indicates that the ambient light of the current monitored scene actually weakens, and the monitoring camera needs to start the infrared light supplement lamp, the cloud server generates an infrared light supplement instruction, and sends the infrared light supplement instruction to the controller of the monitoring camera through wireless communication, thereby controlling to start the infrared light supplement lamp.
In this embodiment, after step S5, that is, after the infrared supplementary lighting instruction is sent to the controller of the monitoring camera to turn on the infrared supplementary lighting, the ambient light detection method of this embodiment further includes:
and calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity.
And judging whether the light intensity error value exceeds a preset error threshold value.
And if the light intensity error value exceeds a preset error threshold value, generating a light intensity correction instruction, and sending the light intensity correction instruction to the light ray sensor.
In this embodiment, the light intensity error value refers to a difference value between a theoretical illumination intensity and an actual illumination intensity in the current monitoring scene, and the unit is lux; the error threshold is a judgment threshold set for the light intensity error; the light intensity correction command refers to a control command for correcting the detection of the light sensor.
Specifically, a prestored error threshold value is obtained from the database, whether the illumination error value between the theoretical illumination intensity and the actual illumination intensity is lower than the error threshold value or not is judged, and when the illumination error value exceeds the error threshold value, the fact that the light ray sensor has overlarge deviation on the light ray detection of the current monitoring scene is shown, the cloud server generates a light intensity correction instruction, and the light intensity correction instruction is sent to the light ray sensor through wireless communication, so that the subsequent detection result of the light ray sensor is more accurate.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The embodiment also provides an environmental light detection system of the security monitoring camera, and the environmental light detection system of the security monitoring camera is in one-to-one correspondence with the environmental light detection methods of the security monitoring camera in the embodiments. As shown in fig. 2, the system for detecting the environmental light of the security monitoring camera includes a detection acquisition module, a first determination module, an image analysis module, a second determination module, and a light supplement module. The functional modules are explained in detail as follows:
the detection acquisition module is used for acquiring the theoretical illumination intensity of the current monitoring scene detected by the light sensor in real time;
the first judgment module is used for judging whether the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement or not based on the theoretical illumination intensity, and the monitoring camera is used for monitoring the current monitoring scene;
the image analysis module is used for acquiring a current monitoring image set of the current monitoring scene if the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement, and performing illumination intensity analysis on the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
the second judgment module is used for judging whether the current monitoring scene meets a second condition that the monitoring camera starts the infrared supplementary lighting or not based on the actual illumination intensity;
and the light supplement module is used for generating an infrared light supplement instruction if the current monitoring scene meets a second condition that the monitoring camera starts infrared light supplement, and sending the infrared light supplement instruction to the controller of the monitoring camera so as to start the infrared light supplement.
Optionally, the first determining module includes:
the first judging unit is used for judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not;
the first determining unit is used for determining that the current monitoring scene meets a first condition that the monitoring camera starts infrared supplementary lighting if the theoretical illumination intensity is lower than a preset first intensity threshold.
Optionally, the image analysis module includes:
the learning analysis unit is used for training and learning the current monitoring image set by using a preset deep learning model and outputting the original illumination intensity of each current monitoring image;
and the statistical unit is used for counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the statistical result, and taking the average illumination intensity as the actual illumination intensity.
Optionally, the second determining module includes:
the second judging unit is used for judging whether the actual illumination intensity is lower than a preset second intensity threshold value, and the second intensity threshold value is smaller than or equal to the first intensity threshold value;
and the second determining unit is used for determining that the current monitoring image meets a second condition that the monitoring camera starts the infrared supplementary lighting if the actual illumination intensity is lower than a preset second intensity threshold.
Optionally, the ambient light detection system of this embodiment further includes:
the error calculation module is used for calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity;
the error judgment module is used for judging whether the light intensity error value exceeds a preset error threshold value or not;
and the correction module is used for generating a light intensity correction instruction if the light intensity error value exceeds a preset error threshold value, and sending the light intensity correction instruction to the light ray sensor.
For specific limitations of the security monitoring camera ambient light detection system, reference may be made to the above limitations on the security monitoring camera ambient light detection method, which is not described herein again. All modules in the security monitoring camera environment light detection system can be completely or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The embodiment also provides a computer device, which may be a server, and the internal structure diagram of the computer device may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing information such as theoretical illumination intensity, actual illumination intensity, a first intensity threshold value, a second intensity threshold value and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the method for detecting the environment light of the security monitoring camera, and the processor executes the computer program to realize the following steps:
acquiring the theoretical illumination intensity of the current monitoring scene detected by a light sensor in real time;
judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene;
if the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
judging whether the current monitoring scene meets a second condition for starting the infrared supplementary lighting of the monitoring camera or not based on the actual illumination intensity;
and if the current monitoring scene meets a second condition that the monitoring camera starts the infrared light supplement, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement.
The present embodiments also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring the theoretical illumination intensity of the current monitoring scene detected by a light sensor in real time;
judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene;
if the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
judging whether the current monitoring scene meets a second condition for starting the infrared supplementary lighting of the monitoring camera or not based on the actual illumination intensity;
and if the current monitoring scene meets a second condition that the monitoring camera starts the infrared light supplement, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (10)
1. A security monitoring camera environment light detection method is characterized in that: the method comprises the following steps:
acquiring the theoretical illumination intensity of the current monitoring scene detected by a light sensor in real time;
judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared supplementary lighting or not based on the theoretical illumination intensity, wherein the monitoring camera is used for monitoring the current monitoring scene;
if the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement, acquiring a current monitoring image set of the current monitoring scene, and analyzing the illumination intensity of the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
judging whether the current monitoring scene meets a second condition that the monitoring camera starts infrared supplementary lighting or not based on the actual illumination intensity;
and if the current monitoring scene meets a second condition that the monitoring camera starts infrared light supplement, generating an infrared light supplement instruction, and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement.
2. The method for detecting the ambient light of the security monitoring camera according to claim 1, wherein: based on the theoretical illumination intensity, whether the current monitoring scene meets a first condition that a monitoring camera starts infrared light supplement is judged, and the method comprises the following steps:
judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not;
and if the theoretical illumination intensity is lower than a preset first intensity threshold value, determining that the current monitoring scene meets a first condition that the monitoring camera starts infrared supplementary lighting.
3. The method for detecting the ambient light of the security monitoring camera according to claim 1, wherein: utilizing a preset deep learning model to analyze the illumination intensity of the current monitoring image set, and acquiring the actual illumination intensity of the current monitoring scene, wherein the method comprises the following steps:
training and learning the current monitoring image set by using a preset deep learning model, and outputting the original illumination intensity of each current monitoring image;
and counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the counting result, and taking the average illumination intensity as the actual illumination intensity.
4. The method for detecting the ambient light of the security monitoring camera according to claim 1, wherein: based on the actual illumination intensity, whether the current monitoring scene meets a second condition that the monitoring camera starts the infrared supplementary lighting is judged, and the method comprises the following steps:
judging whether the actual illumination intensity is lower than a preset second intensity threshold value, wherein the second intensity threshold value is smaller than or equal to the first intensity threshold value;
and if the actual illumination intensity is lower than a preset second intensity threshold value, determining that the current monitoring image meets a second condition that the monitoring camera starts infrared supplementary lighting.
5. The method for detecting the ambient light of the security monitoring camera according to claim 1, wherein: and sending the infrared light supplement instruction to a controller of the monitoring camera so as to start the infrared light supplement, wherein the method further comprises the following steps:
calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity;
judging whether the light intensity error value exceeds a preset error threshold value or not;
and if the light intensity error value exceeds a preset error threshold value, generating a light intensity correction instruction, and sending the light intensity correction instruction to the light ray sensor.
6. The utility model provides a security protection surveillance camera machine ambient light detecting system which characterized in that: the system comprises:
the detection acquisition module is used for acquiring the theoretical illumination intensity of the current monitoring scene detected by the light sensor in real time;
the first judging module is used for judging whether the current monitoring scene meets a first condition that a monitoring camera starts infrared supplementary lighting or not based on the theoretical illumination intensity, and the monitoring camera is used for monitoring the current monitoring scene;
the image analysis module is used for acquiring a current monitoring image set of the current monitoring scene if the current monitoring scene meets a first condition that the monitoring camera starts infrared light supplement, and performing illumination intensity analysis on the current monitoring image set by using a preset deep learning model to acquire the actual illumination intensity of the current monitoring scene;
the second judging module is used for judging whether the current monitoring scene meets a second condition that the monitoring camera starts infrared supplementary lighting or not based on the actual illumination intensity;
and the light supplement module is used for generating an infrared light supplement instruction if the current monitoring scene meets a second condition that the monitoring camera starts infrared light supplement, and sending the infrared light supplement instruction to the controller of the monitoring camera so as to start the infrared light supplement.
7. The security surveillance camera ambient light detection system of claim 6, wherein: the first judging module comprises:
the first judging unit is used for judging whether the theoretical illumination intensity is lower than a preset first intensity threshold value or not;
and the first determining unit is used for determining that the current monitoring scene meets a first condition that the monitoring camera starts the infrared supplementary lighting if the theoretical illumination intensity is lower than a preset first intensity threshold.
8. The security surveillance camera ambient light detection system of claim 6, wherein: the image analysis module includes:
the learning analysis unit is used for training and learning the current monitoring image set by using a preset deep learning model and outputting the original illumination intensity of each current monitoring image;
and the statistical unit is used for counting the original illumination intensity of each current monitoring image, calculating the average illumination intensity of the current monitoring image set according to the statistical result, and taking the average illumination intensity as the actual illumination intensity.
9. The security surveillance camera ambient light detection system of claim 6, wherein: the second judging module includes:
a second judging unit, configured to judge whether the actual illumination intensity is lower than a preset second intensity threshold, where the second intensity threshold is less than or equal to the first intensity threshold;
and the second determining unit is used for determining that the current monitoring image meets a second condition that the monitoring camera starts the infrared supplementary lighting if the actual illumination intensity is lower than a preset second intensity threshold.
10. The security surveillance camera ambient light detection system of claim 6, wherein: the system further comprises:
the error calculation module is used for calculating a light intensity error value between the theoretical illumination intensity and the actual illumination intensity;
the error judgment module is used for judging whether the light intensity error value exceeds a preset error threshold value or not;
and the correction module is used for generating a light intensity correction instruction if the strong error value exceeds a preset error threshold value, and sending the light intensity correction instruction to the light ray sensor.
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